Department of Dermatology and Allergy, Technical University of Munich, Munich, Germany.
Department of Dermatology, University Hospital Muenster, Muenster, Germany.
J Eur Acad Dermatol Venereol. 2023 Nov;37(11):2284-2292. doi: 10.1111/jdv.19325. Epub 2023 Aug 4.
Histopathological differentiation of early mycosis fungoides (MF) from benign chronic inflammatory dermatoses remains difficult and often impossible, despite the inclusion of all available diagnostic parameters.
To identify the most impactful histological criteria for a predictive diagnostic model to discriminate MF from atopic dermatitis (AD).
In this multicentre study, two cohorts of patients with either unequivocal AD or MF were evaluated by two independent dermatopathologists. Based on 32 histological attributes, a hypothesis-free prediction model was developed and validated on an independent patient's cohort.
A reduced set of two histological features (presence of atypical lymphocytes in either epidermis or dermis) was trained. In an independent validation cohort, this model showed high predictive power (95% sensitivity and 100% specificity) to differentiate MF from AD and robustness against inter-individual investigator differences.
The study investigated a limited number of cases and the classifier is based on subjectively evaluated histological criteria.
Aiming at distinguishing early MF from AD, the proposed binary classifier performed well in an independent cohort and across observers. Combining this histological classifier with immunohistochemical and/or molecular techniques (such as clonality analysis or molecular classifiers) could further promote differentiation of early MF and AD.
尽管纳入了所有可用的诊断参数，早期蕈样肉芽肿（MF）与良性慢性炎症性皮肤病的组织病理学鉴别仍然具有挑战性，且往往无法做到。
确定最有影响的组织学标准，以建立一个预测性诊断模型，区分 MF 与特应性皮炎（AD）。
在这项多中心研究中，两名独立的皮肤科病理学家评估了两组明确的 AD 或 MF 患者。基于 32 种组织学属性，开发了一个无假设的预测模型，并在独立的患者队列中进行了验证。
选择了两种组织学特征（表皮或真皮中存在异型淋巴细胞）作为简化模型。在一个独立的验证队列中，该模型对 MF 与 AD 的区分具有很高的预测能力（95%的敏感性和 100%的特异性），并且对个体观察者之间的差异具有稳健性。
该研究调查的病例数量有限，分类器基于主观评估的组织学标准。
为了区分早期 MF 与 AD，所提出的二分类器在独立队列和观察者中表现良好。将这种组织学分类器与免疫组织化学和/或分子技术（如克隆性分析或分子分类器）相结合，可能进一步促进早期 MF 和 AD 的鉴别。
